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Comparing Global and National Approaches to Estimating Deforestation Rates in REDD+ Countries

This working paper aims to bring greater clarity to nontechnical audiences such as climate policymakers by offering a systematic comparison among methods used and forest monitoring results generated by REDD+ countries and global forest monitoring initiatives. First, we review deforestation estimates in 33 national and subnational forest reference emission level (FREL) submissions to the UNFCCC, which are intended to serve as country baseline data for future conservation efforts. We then explain how much and why these results may differ from the globally consistent tree cover loss estimates derived from Hansen et al. (2013). We discuss the potential use of global approaches in terms of accuracy and cost savings, as well as the potential limitations, and offer recommendations.

Key Findings

Executive Summary

Forests have enormous potential to mitigate against climate change and could help the world reach the goals of the 2015 Paris Climate Agreement. Forests soak up CO2 and deforestation releases it. An increasingly diverse menu of methods is being used by countries and international research organizations to monitor forests and estimate rates of deforestation. These multiple methods produce results of various quality, which is a barrier to cross-country comparisons and leads to confusion about which methods and results are most accurate, especially for countries claiming results-based payments for initiatives to halt deforestation. This working paper explains how much and why results differ between nationally reported deforestation estimates and the Global Forest Change (GFC) tree cover loss data of Hansen et al. (2013). Across all REDD+ countries, the GFC data represent an unbiased proxy for tropical deforestation and are produced for a fraction of the cost of what has been invested in national forest monitoring systems. Opportunities to align, adapt, or customize global data for national forest monitoring and reporting may help reduce costs and improve the long-term sustainability and comparability of national systems, while maintaining desired levels of accuracy and national ownership.

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